def get_image(coco,map_source_class_id,class_ids,i,mask_shape,image_size):
annotations = coco.loadAnns(coco.getAnnIds(imgIds=[i], catIds=class_ids, iscrowd=False))
img_url = os.path.join(config.coco_image_dir,coco.imgs[i]["file_name"])
instance_masks = []
cls_ids = []
for annotation in annotations:
class_id = map_source_class_id[annotation['category_id']]
m = annToMask(annotation, coco.imgs[i]["height"],
coco.imgs[i]["width"])
if m.max() < 1:
continue
instance_masks.append(m)
cls_ids.append(class_id)
img = cv2.imread(img_url)
img = cv2.cvtColor(img,cv2.COLOR_BGR2RGB)
cls_ids = np.asarray(cls_ids)
original_shape = img.shape
image, window, scale, padding, crop = utils.resize_image(img,min_dim=image_size,max_dim=image_size)
mask = np.asarray(instance_masks)
mask = np.transpose(mask,axes=[1,2,0])
mask = utils.resize_mask(mask, scale, padding, crop)
image,mask = aug(image,mask)
boxes = utils.extract_bboxes(mask)
mask = utils.minimize_mask(boxes, mask, mini_shape=(mask_shape, mask_shape))
boxes = boxes*1.0 / image_size
return image,cls_ids,boxes,mask
def pull_item(self, idx):
json_pth = self.data[idx]
image_pth = json_pth.replace('.json', '.png')
ig_data = io.imread(image_pth)[:, :, 0:3]
instance_masks = np.zeros(
shape=[self.image_size[0], self.image_size[1], 3])
js_data = json.loads(open(json_pth).read())
msk = []
ids = []
for b in js_data['boundary']:
label = b['correctionType']
if label == 'land':
points = b['points']
p = []
for pp in points:
p.append([pp['pix_x'], pp['pix_y']])
poly_land = np.zeros(
shape=[self.image_size[0], self.image_size[1], 3],
dtype=np.uint8)
cv2.fillPoly(poly_land, np.asarray([p], np.int),
(255, 255, 255))
msk.append(poly_land[:, :, 0:1])
ids.append(0)
if len(msk) > 1:
msk = np.concatenate(msk, axis=2)
elif len(msk) == 1:
msk = msk[0]
else:
return None
ig, window, scale, padding, crop = utils.resize_image_fixed_size(
ig_data, self.image_size)
msk = utils.resize_mask(msk, scale, padding, crop)
if random.randint(0, 1) == 2:
ag = self.aug.to_deterministic()
ig_data = ag.augment_image(ig_data)
msk = ag.augment_image(msk)
box = utils.extract_bboxes(msk)
ids = np.asarray(ids)
mask = np.sum(msk, 2)
mask[np.where(mask > 255)] = 255
box = box / np.asarray([
self.image_size[0], self.image_size[1], self.image_size[0],
self.image_size[1]
])
return ig, box, ids, mask
def pull_item(self, idx):
item_data = self.data[idx]
image_name = item_data['name']
labels = item_data['labels']
label_ix = []
box = []
ig_data = io.imread(os.path.join(self.image_dr, image_name))
instance_masks = []
cls_ids = []
for ll in labels:
category_name = ll['category']
if category_name == 'drivable area':
if ll['attributes']['areaType'] == 'direct':
pts = ll['poly2d']
for x in pts:
direct = np.zeros(shape=ig_data.shape, dtype=np.uint8)
cv2.fillPoly(direct,
[np.asarray(x['vertices'], np.int)],
(255, 255, 255))
cls_ids.append(0)
instance_masks.append(direct[:, :, 0])
elif ll['attributes']['areaType'] == 'alternative':
pts = ll['poly2d']
for x in pts:
alter = np.zeros(shape=ig_data.shape, dtype=np.uint8)
cv2.fillPoly(alter,
[np.asarray(x['vertices'], np.int)],
(255, 255, 255))
cls_ids.append(1)
instance_masks.append(alter[:, :, 0])
mask = np.asarray(instance_masks)
ig, window, scale, padding, crop = utils.resize_image_fixed_size(
ig_data, self.image_size)
if len(labels) == 0:
return
mask = np.transpose(mask, axes=[1, 2, 0])
mask = utils.resize_mask(mask, scale, padding, crop)
image, mask = coco_handler.aug(ig, mask)
boxes = utils.extract_bboxes(mask)
mask = utils.minimize_mask(boxes,
mask,
mini_shape=(self.mask_shape,
self.mask_shape))
#boxes = boxes /np.asarray([self.image_size[1], self.image_size[0],self.image_size[1], self.image_size[0]])
return ig, cls_ids, boxes, mask
def pull_item(self, index):
image_path = self.images[index]
json_path = image_path.replace('.png', '.json')
bundry = json.loads(open(json_path).read().encode('utf8'))
ig = cv2.imread(image_path)
ig = cv2.cvtColor(ig, cv2.COLOR_BGR2RGB)
ig = aug_utils.pytorch_aug_color(ig)
shape = ig.shape[0:2]
total = len(bundry)
msk = np.zeros((shape[0], shape[1], total), dtype=np.uint8)
ids = []
orchard = np.zeros(shape=(shape[0], shape[1], 3), dtype=np.uint8)
msk = []
for idx, b in enumerate(bundry):
if b['correction_type'] == 'tree':
mask = np.zeros(shape=(shape[0], shape[1], 3), dtype=np.uint8)
pts = []
for p in b['boundary']:
pts.append([int(p['x']), int(p['y'])])
pts = np.array(pts, np.int32)
cv2.fillPoly(mask, [pts], color=(255, 255, 255))
msk.append(mask[:, :, 0:1])
ids.append(0)
elif b['correction_type'] == 'orchard':
mask = np.zeros(shape=(shape[0], shape[1], 3), dtype=np.uint8)
pts = []
for p in b['boundary']:
pts.append([int(p['x']), int(p['y'])])
pts = np.array(pts, np.int32)
cv2.fillPoly(mask, [pts], color=(255, 255, 255))
orchard += mask
ig = ig * (orchard / 255)
if len(msk) > 1:
msk = np.concatenate(msk, axis=2)
elif len(msk) == 1:
msk = msk[0]
else:
return None
ig, window, scale, padding, crop = utils.resize_image_fixed_size(
ig, self.image_size)
msk = utils.resize_mask(msk, scale, padding, crop)
if random.randint(0, 1) != 1:
ag = self.aug.to_deterministic()
ig = ag.augment_image(ig)
msk = ag.augment_image(msk)
box = utils.extract_bboxes(msk)
ids = np.asarray(ids)
mj = (box[:, 3] - box[:, 1]) * (box[:, 2] - box[:, 0])
mk = np.where(mj > self.image_size[0] * self.image_size[1] / 32 / 32)
box = box[mk]
ids = ids[mk]
mj = (box[:, 3] - box[:, 1]) / (box[:, 2] - box[:, 0])
mk = np.where(mj > 0.25)
box = box[mk]
ids = ids[mk]
mj = (box[:, 3] - box[:, 1]) / (box[:, 2] - box[:, 0])
mk = np.where(mj < 4)
box = box[mk]
ids = ids[mk]
box = box / np.asarray([
self.image_size[0], self.image_size[1], self.image_size[0],
self.image_size[1]
])
return ig, box, ids
def pull_item(self, index):
image_path = self.images[index]
json_path = image_path.replace('.png', '.json')
bundry = json.loads(open(json_path).read().encode('utf8'))
ig = cv2.imread(image_path)
ig = cv2.cvtColor(ig, cv2.COLOR_BGR2RGB)
shape = ig.shape[0:2]
orchard = []
msk = []
ids = []
for idx, b in enumerate(bundry):
if b['correction_type'] == 'tree':
mask = np.zeros(shape=(shape[0], shape[1], 3))
pts = []
for p in b['boundary']:
pts.append([int(p['x']), int(p['y'])])
pts = np.array(pts, np.int32)
cv2.fillPoly(mask, [pts], color=(255, 255, 255))
msk.append(mask[:, :, 0:1])
ids.append(0)
elif b['correction_type'] == 'orchard':
mask = np.zeros(shape=(shape[0], shape[1], 3))
pts = []
for p in b['boundary']:
pts.append([int(p['x']), int(p['y'])])
pts = np.array(pts, np.int32)
cv2.fillPoly(mask, [pts], color=(255, 255, 255))
orchard.append(mask[:, :, 0:1])
if len(msk) > 1:
msk = np.concatenate(msk, axis=2)
elif len(msk) == 1:
msk = msk[0]
else:
return None
if len(orchard) > 1:
orchard = np.concatenate(orchard, axis=2)
elif len(orchard) == 1:
orchard = orchard[0]
else:
return None
ig, window, scale, padding, crop = utils.resize_image_fixed_size(
ig, self.image_size)
msk = utils.resize_mask(msk, scale, padding, crop)
orchard = utils.resize_mask(orchard, scale, padding, crop)
if random.randint(0, 1) == 1:
ag = self.aug.to_deterministic()
ig = ag.augment_image(ig)
msk = ag.augment_image(msk)
orchard = ag.augment_image(orchard)
box = utils.extract_bboxes(msk)
ids = np.asarray(ids)
mask = np.transpose(msk, [2, 0, 1])
mj = (box[:, 3] - box[:, 1]) * (box[:, 2] - box[:, 0])
mk = np.where(mj < self.image_size[0] * self.image_size[1] / 3 / 3)
box = box[mk]
ids = ids[mk]
mask = mask[mk]
mj = (box[:, 3] - box[:, 1]) * (box[:, 2] - box[:, 0])
mk = np.where(mj > self.image_size[0] * self.image_size[1] / 32 / 32)
box = box[mk]
ids = ids[mk]
mask = mask[mk]
mj = (box[:, 3] - box[:, 1]) / (box[:, 2] - box[:, 0])
mk = np.where(mj > 0.3)
box = box[mk]
ids = ids[mk]
mask = mask[mk]
mj = (box[:, 3] - box[:, 1]) / (box[:, 2] - box[:, 0])
mk = np.where(mj < 3)
box = box[mk]
ids = ids[mk]
mask = mask[mk]
orchard = np.sum(orchard, axis=2)
mask = np.transpose(mask, [1, 2, 0])
#mask = utils.minimize_mask(box, mask, mini_shape=(28, 28))
mask = np.sum(mask, 2)
mask[np.where(mask > 255)] = 255
box = box / np.asarray([
self.image_size[0], self.image_size[1], self.image_size[0],
self.image_size[1]
])
#ig = ig*(orchard/255).astype(np.uint8)
return ig, box, ids, mask